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{extractOz}: A Unified Approach to Extracting Data About Australian Locations Using GPS Points <img src='man/figures/logo.png' align='right' />
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Extract the GRDC agroecological zone, major soil order and weather data
from your GPS sampling points. Datasets for the GRDC agroecological
zones and functions that automatically download modified data from the
Digital Atlas of Australian Soils are included in this package for ease
of use. You may also use your own geospatial vector format file to
extract similar information using the generic function,
extract_area().
Quick Start
You can install {extractOz} like so.
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github("DPIRD-FSI/extractOz", build_vignettes = TRUE)
Load the packages necessary to execute the examples that follow.
library(extractOz)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
Create Locations in WA and NSW
locs <- list(
"Merredin" = c(x = 118.28, y = -31.48),
"Corrigin" = c(x = 117.87, y = -32.33),
"Tamworth" = c(x = 150.84, y = -31.07)
)
Extract the GRDC AgroEcological Zones
See ?extract_ae_zone() for more help on how to use this function.
z <- extract_ae_zone(x = locs)
Extract the Soil Order
See ?extract_soil_order() for more help on how to use this function.
s <- extract_daas_soil_order(x = locs)
Get Weather Data for These Locations in 2020
Using the previously used list of GPS points, fetch weather data from
SILO for 2020. This is just a non-working example, replace
your_api_key with your email address below. See
?extract_patched_point() for more help on how to use this function.
A Note on API Keys
The examples in this README assume that you have stored your API key in your .Renviron file. See Chapter 8 in “What They Forgot to Teach You About R” by Bryan et al. for more on storing details in your .Renviron if you are unfamiliar.
three_sites <-
extract_patched_point(
x = locs,
start_date = "20200101",
end_date = "20201231",
api_key = Sys.getenv("SILO_API_KEY")
)
## You have requested station observation data but some rows in this
## dataset have data codes for interpolated data.
## Check the 'data_source' columns and `get_patched_point()` or
## `get_data_drill()` documentation for further details on codes and
## references.
##
## You have requested station observation data but some rows in this
## dataset have data codes for interpolated data.
## Check the 'data_source' columns and `get_patched_point()` or
## `get_data_drill()` documentation for further details on codes and
## references.
Join the Weather Data with AE Zone, Soil Order and Site Information
Now using dplyr::left_join(), create a single data.frame() of the
location, GPS coordinates, agroecological zone and weather data.
left_join(z, three_sites, by = c(
"location" = "location",
"x" = "x",
"y" = "y"
)) %>%
left_join(s)
## Joining with `by = join_by(location, x, y)`
## Key: <location>
## location x y ae_zone station_code station_name
## <char> <num> <num> <char> <char> <char>
## 1: Corrigin 117.87 -32.33 WA Central 010536 Corrigin
## 2: Corrigin 117.87 -32.33 WA Central 010536 Corrigin
## 3: Corrigin 117.87 -32.33 WA Central 010536 Corrigin
## 4: Corrigin 117.87 -32.33 WA Central 010536 Corrigin
## 5: Corrigin 117.87 -32.33 WA Central 010536 Corrigin
## ---
## 1094: Tamworth 150.84 -31.07 NSW NE/Qld SE 055325 Tamworth Airport AWS
## 1095: Tamworth 150.84 -31.07 NSW NE/Qld SE 055325 Tamworth Airport AWS
## 1096: Tamworth 150.84 -31.07 NSW NE/Qld SE 055325 Tamworth Airport AWS
## 1097: Tamworth 150.84 -31.07 NSW NE/Qld SE 055325 Tamworth Airport AWS
## 1098: Tamworth 150.84 -31.07 NSW NE/Qld SE 055325 Tamworth Airport AWS
## year day date air_tmax air_tmax_source air_tmin air_tmin_source
## <num> <int> <Date> <num> <int> <num> <int>
## 1: 2020 1 2020-01-01 37.4 25 15.3 25
## 2: 2020 2 2020-01-02 23.0 0 16.0 25
## 3: 2020 3 2020-01-03 26.0 0 12.5 0
## 4: 2020 4 2020-01-04 34.0 0 8.0 0
## 5: 2020 5 2020-01-05 38.0 0 12.0 0
## ---
## 1094: 2020 27 2020-12-27 29.8 0 15.0 0
## 1095: 2020 28 2020-12-28 26.8 0 18.3 0
## 1096: 2020 29 2020-12-29 28.9 0 17.5 0
## 1097: 2020 30 2020-12-30 29.4 0 14.9 0
## 1098: 2020 31 2020-12-31 30.0 0 16.1 0
## elev_m et_morton_actual et_morton_actual_source et_morton_potential
## <char> <num> <int> <num>
## 1: 295.0 m 3.3 26 11.0
## 2: 295.0 m 6.4 26 6.4
## 3: 295.0 m 3.4 26 7.3
## 4: 295.0 m 4.6 26 8.4
## 5: 295.0 m 7.4 26 8.8
## ---
## 1094: 394.9 m 5.5 26 6.9
## 1095: 394.9 m 4.1 26 4.9
## 1096: 394.9 m 2.6 26 5.3
## 1097: 394.9 m 6.6 26 6.6
## 1098: 394.9 m 6.2 26 8.1
## et_morton_potential_source et_morton_wet et_morton_wet_source
## <int> <num> <int>
## 1: 26 7.2 26
## 2: 26 6.4 26
## 3: 26 5.4 26
## 4: 26 6.5 26
## 5: 26 8.1 26
## ---
## 1094: 26 6.2 26
## 1095: 26 4.5 26
## 1096: 26 3.9 26
## 1097: 26 6.6 26
## 1098: 26 7.1 26
## et_short_crop et_short_crop_source et_tall_crop et_tall_crop_source
## <num> <int> <num> <int>
## 1: 8.0 26 10.4 26
## 2: 4.4 26 4.7 26
## 3: 5.4 26 6.7 26
## 4: 7.0 26 8.9 26
## 5: 7.3 26 9.0 26
## ---
## 1094: 5.3 26 6.3 26
## 1095: 3.5 26 4.2 26
## 1096: 3.8 26 4.8 26
## 1097: 5.2 26 5.9 26
## 1098: 6.0 26 7.1 26
## evap_comb evap_comb_source evap_morton_lake evap_morton_lake_source
## <num> <int> <num> <int>
## 1: 9.5 25 8.1 26
## 2: 10.4 0 6.6 26
## 3: 8.0 0 6.2 26
## 4: 8.0 0 7.5 26
## 5: 10.6 0 8.4 26
## ---
## 1094: 5.5 25 6.3 26
## 1095: 3.6 25 4.5 26
## 1096: 2.6 25 4.0 26
## 1097: 4.3 25 6.8 26
## 1098: 5.0 25 7.3 26
## evap_pan evap_pan_source evap_syn evap_syn_source extracted
## <num> <int> <num> <int> <Date>
## 1: 9.5 25 11.1 26 2024-07-15
## 2: 10.4 0 7.2 26 2024-07-15
## 3: 8.0 0 8.3 26 2024-07-15
## 4: 8.0 0 10.1 26 2024-07-15
## 5: 10.6 0 10.4 26 2024-07-15
## ---
## 1094: 5.5 25 6.7 26 2024-07-15
## 1095: 3.6 25 4.5 26 2024-07-15
## 1096: 2.6 25 5.1 26 2024-07-15
## 1097: 4.3 25 6.5 26 2024-07-15
## 1098: 5.0 25 7.5 26 2024-07-15
## station_latitude station_longitude mslp mslp_source radiation
## <num> <num> <num> <int> <num>
## 1: -32.3292 117.8733 1012.7 25 31.0
## 2: -32.3292 117.8733 1010.8 0 27.1
## 3: -32.3292 117.8733 1017.2 0 26.8
## 4: -32.3292 117.8733 1020.9 0 30.9
## 5: -32.3292 117.8733 1015.9 0 30.8
## ---
## 1094: -31.0742 150.8362 1015.7 0 24.9
## 1095: -31.0742 150.8362 1012.7 0 15.7
## 1096: -31.0742 150.8362 1011.4 0 14.0
## 1097: -31.0742 150.8362 1014.2 0 26.4
## 1098: -31.0742 150.8362 1015.8 0 28.7
## radiation_source rainfall rainfall_source rh_tmax rh_tmax_source rh_tmin
## <int> <num> <int> <num> <int> <num>
## 1: 42 0.0 0 22.6 26 83.5
## 2: 42 0.0 0 71.2 26 100.0
## 3: 42 0.0 0 37.5 26 87.0
## 4: 42 0.0 0 26.3 26 100.0
## 5: 42 0.0 0 33.4 26 100.0
## ---
## 1094: 42 2.0 0 46.5 26 100.0
## 1095: 42 10.2 0 61.9 26 100.0
## 1096: 42 1.0 0 50.0 26 99.6
## 1097: 42 18.8 0 52.7 26 100.0
## 1098: 42 0.0 0 44.5 26 100.0
## rh_tmin_source vp vp_deficit vp_deficit_source vp_source
## <int> <num> <num> <int> <int>
## 1: 26 14.5 32.7 26 25
## 2: 26 20.0 5.2 26 0
## 3: 26 12.6 14.9 26 0
## 4: 26 14.0 22.7 26 0
## 5: 26 22.1 24.1 26 0
## ---
## 1094: 26 19.5 14.3 26 0
## 1095: 26 21.8 9.3 26 0
## 1096: 26 19.9 13.8 26 0
## 1097: 26 21.6 11.6 26 0
## 1098: 26 18.9 15.8 26 0
## daas_soil_order
## <char>
## 1: Sodosol
## 2: Sodosol
## 3: Sodosol
## 4: Sodosol
## 5: Sodosol
## ---
## 1094: Dermosol
## 1095: Dermosol
## 1096: Dermosol
## 1097: Dermosol
## 1098: Dermosol
Code of Conduct
Please note that the {extractOz} project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.